Evidence map›Paper›PMID 39910074›Full record

ArticleNPJ science of learning2025

Exploring the potential of LLM to enhance teaching plans through teaching simulation.

Bihao Hu, Jiayi Zhu, Yiying Pei, Xiaoqing Gu

Abstract read
In one paragraph

Article in NPJ science of learning, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bihao HuSchool of Computer Science and Technology, East China Normal University, Shanghai, China.ORCID http://orcid.org/0009-0008-8852-7793
Jiayi ZhuShanghai Weiyu High School, Shanghai, China.
Yiying PeiDepartment of Education, East China Normal University, Shanghai, China.
Xiaoqing GuDepartment of Education Information Technology, East China Normal University, Shanghai, China. xqgu@ses.ecnu.edu.cn.ORCID http://orcid.org/0000-0001-8256-5408

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62477013
6 · The paper itself

Abstract

The introduction of large language models (LLMs) may change future pedagogical practices. Current research mainly focuses on the use of LLMs to tutor students, while the exploration of LLMs' potential to assist teachers is limited. Taking high school mathematics as an example, we propose a method that utilizes LLMs to enhance the quality of teaching plans through guiding the LLM to simulate teacher-student interactions, generate teaching reflections, and subsequently direct the LLM to refine the teaching plan by integrating these teaching process and reflections. Human evaluation results show that this method significantly elevates the quality of the original teaching plans generated directly by LLM. The improved teaching plans are comparable to high-quality ones crafted by human teachers across various assessment dimensions and knowledge modules. This approach provides a pre-class rehearsal simulation and ideas for teaching plan refinement, offering practical evidence for the widespread application of LLMs in teaching preparation.

Identifiers

PMID39910074
PMCPMC11799216

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.